Opposing brain signatures of sleep in task-based and resting-state conditions

Sleep and depression have a complex, bidirectional relationship, with sleep-associated alterations in brain dynamics and structure impacting a range of symptoms and cognitive abilities. Previous work describing these relationships has provided an incomplete picture by investigating only one or two types of sleep measures, depression, or neuroimaging modalities in parallel. We analyze the correlations between brainwide neural signatures of sleep, cognition, and depression in task and resting-state data from over 30,000 individuals from the UK Biobank and Human Connectome Project. Neural signatures of insomnia and depression are negatively correlated with those of sleep duration measured by accelerometer in the task condition but positively correlated in the resting-state condition. Our results show that resting-state neural signatures of insomnia and depression resemble that of rested wakefulness. This is further supported by our finding of hypoconnectivity in task but hyperconnectivity in resting-state data in association with insomnia and depression. These observations dispute conventional assumptions about the neurofunctional manifestations of hyper- and hypo-somnia, and may explain inconsistent findings in the literature.

Reviewer #1 (Remarks to the Author): The manuscript by Abdelhack and colleagues utilized the large cohort of UK biobank to investigate the association between brain function and sleep quality measured by self-report questionnaire and accelerometer.Cognitive function and depressive symptoms were also utilized to characterize the brain-wide patterns.They found neural signatures of insomnia and depression were negatively correlated with neural signatures of sleep duration in the task condition but positively correlated in the resting-state condition.These findings suggested contradictory patterns of resting-state and task-based activation in response to poor sleep.The authors developed clear hypotheses to explain the results and my concerns were with the introduction and the reporting of the findings.Major: 1) The introduction of the manuscript should be carefully revised to be more logical and clearer.The connections of the idea are unclear therefore it's hard to follow the main idea.Meanwhile, the description of previous work on neuroimaging (fourth and fifth paragraph) is seemly too long compared with the first three paragraphs.
2) Considering the classification accuracies were utilized as a proxy for cortical activation in response to the visual stimuli, careful validation should be performed to demonstrate the effectiveness of this index.Meanwhile, the UK biobank also provided the task fMRI data of face activation, why not directly utilize the measure of task-based fMRI provided by UKB?
3) The accelerometer-based sleep duration was mainly utilized to measure the association with brain function.Previous studies showed a lower association between self-reported sleep duration and accelerometer-based sleep duration.Could the results in the manuscript be validated by the self-reported sleep duration?4) Previous study showed a nonlinear association between sleep duration and mental health and also cognitive function, the authors also found similar neural pattern between long sleepers and depressive symptoms only in task-based data which were worth well discussing.5) In Figure 6c, sleep duration showed a consistent positive association with brain connectivity both in rest and task state, while insomnia and depressive symptoms showed distinct association direction with brain connectivity in the task and rest state.Please classify the possible mechanisms and discuss them.Minor: 1) The p value of the main results and the adjustments for the multiple comparisons should be briefly stated in the results section.There were no p values of the associations on page 9 and 12.
2) Some sentences in the abstract and results are not so clear.Please revise it.
Reviewer #2 (Remarks to the Author): Abdelhack et al. analyzed the correlations between task and resting-state brain-wide signatures of sleep, cognition, and depression in over 30,000 individuals.They found that neural signatures of insomnia and depression were negatively correlated with neural signatures of sleep duration in the task condition but positively correlated in the resting-state condition, suggesting opposing brain signatures of sleep in task-based and resting-state conditions.Overall, this study is interesting and the manuscript is well-written.The strengths include a large sample size, elaborate methodologies, and validation in an independent sample.I have some concerns during the review.1.The authors should verify or provide evidence for the validity of the questionnaires they used to assess sleep, cognition, and depression.2.The flowchart in Figure 1 is a little confusing.Actually, correlations between each neuroimaging modality and all behavioral phenotypes were tested.3.The reviewer wonder whether the results will be present when other task-based fMRI data are adopted.4.The authors should specify the rationale for investigating the correlations of neural signatures (i.e., spatial correlations between association statistics) of sleep, depression, and cognition taskactivated vs. resting conditions.What did the positive and negative correlation mean?5.Did the authors consider spatial auto-correlations?6.The authors observed that brain regions were hyperconnected under the resting condition with depression and insomnia but hypoconnected during the task condition.They may directly examine the correlations between these affected functional connectivity to show that they were inversely correlated during task and resting conditions.7.When exploring the correlations between brain connectivity and behavioral phenotypes, connectome predictive modeling (CPM) is strongly recommended.8.The conclusions (opposing brain signatures of sleep in task-based and resting-state conditions) appear overstated based on the current findings.

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In this letter, original reviewer comments are provided in bold, and author responses are indicated in normal font.Additions to the text of the manuscript are provided in green.

Reviewer #1 (Remarks to the Author):
The

Major:
The introduction of the manuscript should be carefully revised to be more logical and clearer.The connections of the idea are unclear therefore it's hard to follow the main idea.Meanwhile, the description of previous work on neuroimaging (fourth and fifth paragraph) is seemly too long compared with the first three paragraphs.
We have revised and restructured the introduction section to improve its clarity and logical structure.We believe it is now more balanced between concepts and sets up the results section more directly.

Considering the classification accuracies were utilized as a proxy for cortical activation in response to the visual stimuli, careful validation should be performed to demonstrate the effectiveness of this index. Meanwhile, the UK biobank also provided the task fMRI data of face activation, why not directly utilize the measure of task-based fMRI provided by UKB?
Thank you for this comment -we agree that metric validation is important.The use of Multivariate Pattern Analysis (MVPA) is a standard process that was chosen for its improved sensitivity to distributed coding, where the brain stimulus response is distributed among many voxels within a region, compared to univariate analysis that is sensitive to mean change of global signals [1][2][3][4] .The latter (univariate) is the one provided by the UK Biobank team.The reasoning behind MVPA's superiority is that it selects the voxels which are most related to the task within a region and aggregates their contribution.This means it can integrate information from voxels that are considered by the univariate analysis as sub-threshold.Additionally, it will also discard the effect of voxels that are not contributing to the task classification.One disadvantage to this method is its sensitivity to noise 5 , though this concern is mitigated by our approach of cross-validation where the classification accuracy is reported only on withheld test sets which are averaged between folds.To make our motivation more clear, we have added the following text to our methods section citing relevant publications: "Multivariate methods have an advantage over the readily available univariate analyses in that they select voxels relevant to the task and aggregate their effect leading to more sensitivity to the classification target in cases of distributed coding 1-4 " To provide additional validation, as requested, we performed confirmation analyses using the univariate features provided directly by UK Biobank.We have added a new figure to the supplementary information (See below, also Figure S8 in supplementary information).As expected, these analyses yielded somewhat fewer significant associations with our selected phenotypes; only cognition showed significant associations with the region-averaged beta values (Figure S8A).Subsequent correlation analysis results were largely consistent with the MVPA-based results except for four values between duration of longest sleep bout and insomnia and between self-reported daytime dozing and PHQ-2, cognition, and insomnia (See figure below, also in supplemental information Figure S8B).Given that the majority of the imaging feature-to-phenotype associations from the univariate analysis were not significant, the correlation values themselves should not be overinterpreted, as they largely represent the product of noise in voxels that are not related to the input and are removed by MVPA.
We have added the following text to the results section to reflect the results from our new univariate analysis: "Self-reported daytime dozing frequency showed no significant associations in any region.Full table of statistical results is provided in supplementary file 2. We also tested the associations using univariate analysis of faces vs. shapes contrasts but associations were non-significant for all phenotypes except for cognition (Figure S8) indicating the distributed nature of signals across regions."important than total sleep duration when determining activation in the task condition.This was also observed in our independent replication dataset from the Human Connectome Project, where sleep duration data were also derived from self-report (i.e.PSQI duration of sleep).Given the above results we have added the following text to the first subsection of results: "Accelerometer-measured sleep quality was weakly correlated with cognitive performance (r=0.036;p=5.39×10 -3 ) while depressive symptoms were correlated with self-reported insomnia (r=0.15;p=5.64×10 -63 ), both in positive directions (Figure 1A).Self-reported insomnia and daytime dozing frequencies were also positively correlated, though the magnitude of this correlation was similarly very small, with only 0.7% of variance explained (r=0.081;p=2.25×10 -

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).As expected, the accelerometer-measured duration of longest sleep bout had negative correlations with both self-reported insomnia (r=-0.072;p=2.21×10 -15 ) and self-reported daytime dozing (r=-0.11;p=1.29×10 -35 ), again with very small effect sizes.Self-reported sleep duration had a moderate but significant correlation with the accelerometer-measured duration of longest sleep bout (Table S5)." We have also added the following text to the third subsection of results: "To further confirm these findings, we performed a similar analysis on the independent HCP dataset, which included self-reported sleep measurements using PSQI 26 , sadness (proxy for depression) measured using the NIH toolbox 37 , and cognition measured by the Mini-Mental State Examination (MMSE) 38 .Results for both task-based and resting-state data were largely in agreement, with the exception of neural signatures for cognition measures (Figure 4C, D).
Correlations between the associations of anatomical models were largely consistent with those from the task fMRI experiment (Figure S7C).Additionally, we tested the correlation of selfreported sleep duration for task, resting-state, and anatomical measures and it was highly correlated with duration of longest sleep but only for the resting-state data with non-significant correlation for task-based and anatomical data (Table S5)." Previous study showed a nonlinear association between sleep duration and mental health and also cognitive function, the authors also found similar neural pattern between long sleepers and depressive symptoms only in task-based data which were worth well discussing.
We appreciate this comment.Indeed the non-linear effects we identified can be interpreted in the context of previous work.Accordingly, we have added acknowledgement of these effects and relevant citations to the discussion: "In our population, the groups of short sleepers (duration of longest sleep bout < 6.8 hours) showed an inverted association with insomnia which is reasonable but it signals that insomnia neural signature is multimodal resembling both short and long sleep.There was no significant association between duration of longest sleep bout and PHQ-2 in that group.The positive associations between duration of longest sleep bout remained consistent between depressed and not-depressed groups while insomnia association was insignificant for the depressed group.The non-depressed group showed identical associations with the whole cohort which could be explained by the fact that the non-depressed group represented the majority of the cohort.These results are consistent with previous studies that show a non-linear relationship between sleep duration and depression with sleep close to seven hours being considered optimal [7][8][9] ." In Figure 6c, sleep duration showed a consistent positive association with brain connectivity both in rest and task state, while insomnia and depressive symptoms showed distinct association direction with brain connectivity in the task and rest state.Please classify the possible mechanisms and discuss them.
Thank you very much for this comment.Indeed, we did not comment on this due to the subthreshold p-values of the average connectivity.However, it is an interesting result that contributes helpfully to the narrative of our study.We have now added a section in the results about possible mechanisms that could be driving these results as follows: "Self-reported daytime dozing showed a strongly negative association suggesting a strong hypoactivation in the resting condition.Duration of longest sleep bout had positive associations for both task and resting conditions, especially in the frontoparietal and attention networks but the average effect was not significant (Figure 6C).The connectivity association patterns in task and resting state were not related to each other but the effect was observed on the global mean level only (Figure S10)."

Minor:
The p value of the main results and the adjustments for the multiple comparisons should be briefly stated in the results section.There were no p values of the associations on page 9 and 12.
One challenge with presenting these p-values in the results section is that there are many regions explicitly mentioned, so we feel that exhaustively listing statistics would affect readability.To help clarify and improve transparency, we have now added p-values for the top associated regions to the main text.Additionally, for full disclosure, we have added two supplementary files that include all summary statistics from the analyses in Figures 2, 3, and 6.These are referenced as supplementary files 2-6 in the results section.Some sentences in the abstract and results are not so clear.Please revise it.
Thank you very much for flagging these issues.We scanned the abstract and results sections for unclear phrases and attempted to clarify any ambiguity.We believe the revised text is much improved.

Reviewer #2 (Remarks to the Author):
Abdelhack et al. analyzed the correlations between task and resting-state brain-wide signatures of sleep, cognition, and depression in over 30,000 individuals.They found that neural signatures of insomnia and depression were negatively correlated with neural signatures of sleep duration in the task condition but positively correlated in the restingstate condition, suggesting opposing brain signatures of sleep in task-based and resting-state conditions.Overall, this study is interesting and the manuscript is wellwritten.The strengths include a large sample size, elaborate methodologies, and validation in an independent sample.I have some concerns during the review.

The authors should verify or provide evidence for the validity of the questionnaires they used to assess sleep, cognition, and depression.
Thank you for this comment that gives us the opportunity to provide valuable context.The questionnaires that were used in the UK Biobank are standards (or equivalent to standards) in their respective fields.Self-reported sleep questions in UK Biobank ask participants to provide general tendencies of insomnia, sleep duration, and daytime dozing.It contains instructions to provide data from the past four weeks in case there is a large variability in those patterns.This is similar to the Pittsburgh Sleep Quality Index 10 where the participant is asked to provide insomnia and daytime dozing habits among other sleep factors for the period in the month before the questionnaire.It is the most common sleep quality index 11 and was confirmed by multiple studies to have internal reliability and validity 12 .The depression questions represent the Given that the correlation is calculated in a region-wide manner, the auto-correlation is always one.
We also considered the spatial correspondence for associations in different regions for the association maps in the task condition and the anatomical cortical thickness.To confirm that the correlations are not due to a baseline correlation between regions, we conducted a spin test with 1000 permutations for each hemisphere 16 .Given our regions are bilateral, we conducted spin tests for each hemisphere independently and then pooled the results.We have now updated our results to include both the p-values for both the correlation (beta) distribution test and the spin test.The results from both tests were highly consistent with only one correlation yielding a significant p-value on one test but not the other.We have also a) added a new subsection in the methods to describe the second order correlations with all statistical testing details as shown below, b) added the spin test p-values to the results section, and c) updated figure 4 (see below) to reflect the values that were significant in both tests.
"Second order correlations testing similarity of neural signatures We correlated the beta coefficients of the models for duration of longest sleep bout, PHQ-2, cognition, self-reported dozing, and insomnia with resting state connectivity and task-based activation maps and cortical thickness in the HCP space 6 with each other.p-values were calculated on the probability that the beta distribution is drawn from a distribution with zero correlation.To correct for multiple comparisons we used Bonferroni correction (pbeta < 0.05/5).Additionally, to correct for auto-correlations for region-based maps in task and anatomical association correlations, we conducted a spin test 16 .Since our results are bilateral, we conducted 1000 permutations of the results with each of the hemispheres and calculated the pvalues as the percentage of permutations yielding correlation values with a norm higher than the correlation value.We also corrected for multiple comparisons using Bonferroni correction (pspin < 0.05/5).We report the correlation value as significant if it is significant for both statistical tests." manuscript by Abdelhack and colleagues utilized the large cohort of UK biobank to investigate the association between brain function and sleep quality measured by selfreport questionnaire and accelerometer.Cognitive function and depressive symptoms were also utilized to characterize the brain-wide patterns.They found neural signatures of insomnia and depression were negatively correlated with neural signatures of sleep duration in the task condition but positively correlated in the resting-state condition.These findings suggested contradictory patterns of resting-state and task-based activation in response to poor sleep.The authors developed clear hypotheses to explain the results and my concerns were with the introduction and the reporting of the findings.

Table S5 :
Correlation table for self-reported sleep duration with the five main phenotypes for all tested modalities